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# Machine Learning Engineer

- **Company:** Smadex SLU
- **Location:** Barcelona, Spain (Remote available)
- **Salary:** €48,000.0 - €60,000.0
- **Contract:** Permanent contract
- **Skills:** Biometrics, Monitoring of Systems, Machine Learning, Data Strategy, Document Classification
- **Published:** July 17, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

+ Seven or more years of experience in product ownership, product management, or equivalent delivery focused roles.
 + Demonstrated experience supporting ML based products in production.
 + Direct experience working with data science and ML engineering teams.
Domain and Technical FluencyStrong working knowledge of computer vision and ML fundamentals.Experience with biometric technologies such as face matching, liveness detection, and spoof prevention.Experience with document verification, document classification, or document fraud detection.Hands-on experience building ML based products in the biometric and document identity space is highly valuable.Ability to evaluate tradeoffs between different modeling and data approaches without being a data scientist.
Execution and JudgmentComfortable pushing back on ML teams when solutions are over engineered, misaligned, or not production ready.Able to propose alternate approaches grounded in data availability, fraud realities, and delivery constraints.Strong attention to detail and a bias toward shipping reliable and measurable capabilities.
Communication SkillsAble to clearly articulate ML concepts, risks, and tradeoffs to non technical stakeholders.Comfortable supporting customer facing or internal discussions around model behavior and limitations.Able to document requirements and acceptance criteria with precision.What Would Be Nice (Preferred Experience):

 + Hands on experience shipping biometric or document based ML solutions into production.
 + Experience in fraud, identity, or regulated environments such as financial services or fintech.
 + Familiarity with model monitoring, drift management, and feedback loops.
 + Experience working with global document types and jurisdictional variation.

## Description

We are seeking a Senior Product Owner to drive execution of machine learning based capabilities across biometric authentication and document verification. This role is deeply embedded with machine learning, engineering, and fraud teams, ensuring initiatives are clearly defined, well scoped, and delivered into production with measurable impact. This is a hands-on delivery role, not a strategy ownership position. The ideal candidate has direct experience building and operating ML driven products in biometric and document environments, understands how these systems behave in production, and is comfortable challenging assumptions, proposing alternatives, and pushing back when needed to ensure outcomes align with fraud risk, customer needs, and operational realities.What You Will Do (Essential Responsibilities):

 + ML Feature and Capability Ownership
 + Own and manage the backlog for ML-driven biometric and document verification capabilities.
 + Translate fraud, identity, and customer requirements into clear and actionable ML work items.
 + Partner closely with ML engineers and data scientists to refine problem statements into feasible deliverables.
 + Define acceptance criteria that reflect real world performance, not just offline model metrics.
Embedded ML Team CollaborationServe as the primary product owner for ML and data science teams.Participate actively in model design discussions, prioritization, and tradeoff analysis.Challenge scope, timelines, and modeling approaches when misaligned with business or risk objectives.Propose alternate ideas across data strategy, modeling approaches, workflow design, or deployment patterns.
Production Readiness and Lifecycle SupportSupport model lifecycle activities including training, evaluation, deployment, and retraining.Ensure monitoring, drift detection, and feedback loops are incorporated into delivery plans.Help define rollout, experimentation, and rollback guardrails.
Data and Labeling ExecutionPartner with agent operations and data teams on labeling strategy and data quality.Help define labeling schemas and workflows to support effective model training.Identify risks related to label noise, bias, or insufficient coverage across geographies and document types.
Fraud and Adversarial AwarenessIncorporate fraud patterns and adversarial thinking into backlog prioritization.Ensure features and models are resilient to evolving attack vectors such as spoofs, deepfakes, and injection attacks.Support layered and defense in depth approaches rather than single model dependency.
Cross Functional CoordinationWork closely with engineering, fraud, compliance, legal, and customer teams.Support internal and external conversations where ML behavior or performance needs explanation.Translate technical constraints into clear delivery expectations for non technical stakeholders.What You Will Need (Required Knowledege, Skills & Abilities)


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